Performance, Personality, and Energetics: Correlation, Causation, and Mechanism
Bibliographic record
Abstract
The study of phenotypic evolution should be an integrative endeavor that combines different approaches and crosses disciplinary and phylogenetic boundaries to consider complex traits and organisms that historically have been studied in isolation from each other. Analyses of individual variation within populations can act to bridge studies focused at the levels of morphology, physiology, biochemistry, organismal performance, behavior, and life history. For example, the study of individual variation recently facilitated the integration of behavior into the concept of a pace-of-life syndrome and effectively linked the field of energetics with research on animal personality. Here, we illustrate how studies on the pace-of-life syndrome and the energetics of personality can be integrated within a physiology-performance-behavior-fitness paradigm that includes consideration of ecological context. We first introduce key concepts and definitions and then review the rapidly expanding literature on the links between energy metabolism and personality traits commonly studied in nonhuman animals (activity, exploration, boldness, aggressiveness, sociability). We highlight some empirical literature involving mammals and squamates that demonstrates how emerging fields can develop in rather disparate ways because of historical accidents and/or particularities of different kinds of organisms. We then briefly discuss potentially interesting avenues for future conceptual and empirical research in relation to motivation, intraindividual variation, and mechanisms underlying trait correlations. The integration of performance traits within the pace-of-life-syndrome concept has the potential to fill a logical gap between the context dependency of selection and how energetics and personality are expected to interrelate. Studies of how performance abilities and/or aspects of Darwinian fitness relate to both metabolic rate and personality traits are particularly lacking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".